Writer identification using machine learning approaches: a comprehensive review

Rehman, Arshia; Naz, Saeeda; Razzak, Muhammad Imran

doi:10.1007/s11042-018-6577-1

Cited by 69 publications

(34 citation statements)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from other methods [20][21][22][23][24], deep learning is another approach which can be used for writer identification. Convolutional neural network (CNN) is a one of the deep learning models that can be employed.…”

Section: Vinita Balbhim Patil Rajendra R Patilmentioning

confidence: 99%

Writer Identification with Hybrid Model using Hybrid HMM and ANN

Patil*¹,

Patil²

2019

IJRTE

View full text Add to dashboard Cite

In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared

show abstract

Section: Vinita Balbhim Patil Rajendra R Patilmentioning

confidence: 99%

Writer Identification with Hybrid Model using Hybrid HMM and ANN

Patil*¹,

Patil²

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…It showed a positive correlation between the response and customer satisfaction. The authors of Reference [ 17 ] had reviewed the classification techniques, including deep and convolutional, to identify the writer from their handwriting. They discussed several challenges in identification related to language characteristics, scripts, and the lack of datasets.…”

Section: Introductionmentioning

confidence: 99%

Applications of Big Data Analytics to Control COVID-19 Pandemic

Alsunaidi

Almuhaideb

Ibrahim

et al. 2021

Sensors

View full text Add to dashboard Cite

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

show abstract

“…Till now, Personality disorders are studied from two perspectives. Major studies are done from psychological and psychiatric aspects [8] [9]. To study personality disorder from psychological perspective psychologists concentrate on behavior aspects and this is done by set of lengthy structured interviews, however psychiatry study personality disorders based on mental feature like amygdale, fMRI etc.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the advancement of technology handwriting analysis is done with the help of computers and it is used extensively in various applications. Numerous studies considered handwriting as a biometric behavior and due to this it can be considered as an identification tool [17].There is lot of research available to predict personality characteristics using handwriting [3,5,9,[7][8][9][10][11][12][13][14]however the research to identify violent behavior in handwriting is scanty. According to studies conducted by [14] states that extremely larger alphabets in handwriting sample should be considered as red flag.…”

Section: Introductionmentioning

confidence: 99%

Identification of violent behavior using Handwriting

Jain¹

2020

IJATCSE

View full text Add to dashboard Cite

Violent behavior in one's personality is hidden aspect as it has some roots in writer's subcoscious and is reflected in one's handwriting. Identifying violent behavior has its importance as it is closely related with personality disorders and its associated crimes. Identifying violence behavior in handwriting is crucial as it can prohibit future crimes. Graphology or handwriting analysis is a scientific method to identify, evaluate and understand the personality of an individual through patterns revealed by handwriting. Handwriting analysis is done by skilled graphologists however, with the advancement of technology we are able to facilitate the task of human specialists by processing handwritten images. This paper proposes a novel approach to identify violent and aggressive behavior through handwriting. The aim of this research is to study and examine the relationship between handwriting and violent behavior. The graphic handwriting characteristics of total 60 samples, were taken for the study including 30 for normal and 30 for violent handwriting samples. The samples of violent handwriting were analyzed and compared with normal handwriting samples. Some potential features in handwriting were found which were useful to identify violent and aggressive behavior.

show abstract

Writer identification using machine learning approaches: a comprehensive review

Cited by 69 publications

References 133 publications

Writer Identification with Hybrid Model using Hybrid HMM and ANN

Writer Identification with Hybrid Model using Hybrid HMM and ANN

Applications of Big Data Analytics to Control COVID-19 Pandemic

Identification of violent behavior using Handwriting

Contact Info

Product

Resources

About